FolderWise - AI File Organizer That Actually Reads Your Files Before Moving Them
Your Downloads folder is a crime scene and Finder's search is basically useless for 'that contract from last March.' FolderWise watches your messy folders, reads file contents via embeddings, and auto-proposes a clean structure you approve with one click. No more grep, no more manual sorting, no more hunting for that pitch deck.
Difficulty
intermediate
Category
Productivity
Market Demand
High
Revenue Score
7/10
Platform
Desktop App
Vibe Code Friendly
No
Hackathon Score
🏆 7/10
Validated by Real Pain
— seeded from real developer complaints
Knowledge workers frequently describe frustration with existing file search tools that match only filenames, not what documents actually contain, forcing them to manually sort files or remember exact naming conventions they set months ago.
What is it?
Knowledge workers on r/productivity and Hacker News threads consistently complain that macOS Spotlight finds file names but not semantic content — if you don't remember what you called a file, you're lost. FolderWise runs as a lightweight macOS menu bar app, watches specified folders, embeds file contents via OpenAI embeddings, clusters semantically similar files, and presents a proposed folder structure the user approves before any file moves. Think of it as Marie Kondo but she actually reads your contracts before deciding they spark joy. The $9.99/month or $99 lifetime model targets knowledge workers who deal with 50-plus files per week. Buildable now because macOS file system watching via FSEvents is stable, OpenAI embeddings are cheap at $0.0001 per 1k tokens, and Electron or Tauri gives a fast cross-platform shell.
Why now?
OpenAI embeddings dropped to $0.0001 per 1k tokens in 2024, making per-file semantic indexing practically free, and Tauri v2 now makes shipping a macOS app from TypeScript trivial — this cost and complexity barrier only just disappeared.
- ▸Folder watcher using chokidar that indexes new files automatically without user action.
- ▸Semantic content embedding via OpenAI that understands what a file IS, not just its name.
- ▸One-click approve-or-reject proposed folder structure before any file actually moves.
- ▸Natural language file search across all indexed files using cosine similarity.
Target Audience
Knowledge workers, freelancers, and consultants managing 50-plus files per week — roughly 5M macOS power users actively complaining about folder chaos.
Example Use Case
Jordan, a freelance consultant juggling 8 client projects, points FolderWise at his Downloads folder, approves the AI-proposed structure in 30 seconds, and finds any file by typing a plain sentence into the semantic search bar instead of remembering file names.
User Stories
- ▸As a freelance consultant, I want my Downloads folder auto-organized by client and project type, so that I stop losing files before client calls.
- ▸As a knowledge worker, I want to search my files with a plain sentence, so that I find documents without remembering their exact name.
- ▸As a privacy-conscious user, I want file content processed locally before any API call, so that my confidential documents never leave my machine in raw form.
Acceptance Criteria
File Watcher: done when new file dropped in watched folder appears in index within 5 seconds. Embedding: done when each file has a stored vector and proposed folder label. Proposal UI: done when user can approve or reject each group without any file moving prematurely. Search: done when typing a plain sentence returns the correct file within top 3 results.
Is it worth building?
$99 license x 100 buyers = $9,900 in month 1. $9.99/month x 200 subscribers = $1,998 MRR by month 3. Realistic via ProductHunt and Twitter/X.
Unit Economics
CAC: $8 via community demos and ProductHunt. LTV: $99 one-time or $120 (12 months at $9.99/month). Payback: immediate. Gross margin: 90% after API costs.
Business Model
One-time $99 license or $9.99/month subscription via Stripe.
Monetization Path
Gumroad or Stripe Payment Links for one-time license, Stripe subscriptions for monthly plan, with a 14-day free trial to reduce friction.
Revenue Timeline
First dollar: week 3 beta license. $1k MRR: month 2. $5k MRR: month 6. $10k MRR: month 12.
Estimated Monthly Cost
OpenAI Embeddings API: $20, Vercel for landing page: $10, Supabase for license keys: $10, Stripe fees: $15. Total: ~$55/month at launch.
Profit Potential
Solid side income at $3k-$8k MRR, lifestyle-business level.
Scalability
Medium — add Windows support, team shared vaults, and Notion/Obsidian sync in V2.
Success Metrics
Week 2: 50 beta downloads. Month 1: 30 paid licenses. Month 3: 80 licenses plus 50 subscribers.
Launch & Validation Plan
Post a screen recording of the tool organizing a messy Downloads folder to r/macapps and r/productivity, collect 50 waitlist signups before shipping the binary.
Customer Acquisition Strategy
First customer: share a 60-second Loom of the app live-organizing a chaotic folder in r/macapps and offer 20 free beta keys for feedback. Ongoing: ProductHunt launch, Twitter/X demos, SEO for 'mac file organizer AI', Hacker News Show HN post.
What's the competition?
Competition Level
Low
Similar Products
Hazel (rule-based, no semantic understanding), DEVONthink (powerful but $100 upfront and complex), macOS Spotlight (name search only, no content clustering). FolderWise fills the semantic content gap all three miss.
Competitive Advantage
Reads file content semantically — not just file names like Hazel or file tags like DEVONthink — and requires zero manual tagging.
Regulatory Risks
Low regulatory risk. Files are processed locally; only embeddings (not raw content) are sent to OpenAI API. Document this clearly in privacy policy.
What's the roadmap?
Feature Roadmap
V1 (launch): folder watcher, semantic clustering, approve/reject UI, natural language search. V2 (month 2-3): Windows support, custom taxonomy rules, Obsidian vault sync. V3 (month 4+): team shared index, cloud backup of embeddings, Notion integration.
Milestone Plan
Phase 1 (Week 1-2): file watcher, embedder, and SQLite index working locally. Phase 2 (Week 3): proposal UI, approve/reject logic, and Stripe license gate deployed. Phase 3 (Month 2): ProductHunt launch with 50 beta signoffs and 30 paid licenses.
How do you build it?
Tech Stack
Tauri (Rust shell), TypeScript frontend, OpenAI Embeddings API, SQLite via better-sqlite3, chokidar for file watching — build with Cursor for Rust/TS backend, v0 for UI components.
Suggested Frameworks
Tauri, chokidar, OpenAI Node SDK
Time to Ship
3 weeks
Required Skills
Tauri basics, OpenAI embeddings API, SQLite, file system events on macOS.
Resources
Tauri docs, OpenAI embeddings guide, chokidar README, SQLite quickstart.
MVP Scope
src-tauri/main.rs (file watcher shell), src/App.tsx (approval UI), src/lib/embedder.ts (OpenAI embeddings), src/lib/db.ts (SQLite index), src/lib/organizer.ts (cluster and propose), src/components/FileCard.tsx, src/components/ProposalView.tsx, tauri.conf.json, .env, README.
Core User Journey
Download app -> select watched folder -> view AI-proposed structure -> approve with one click -> files organized in seconds.
Architecture Pattern
chokidar watches folder -> new file detected -> content read -> OpenAI embeddings API -> vector stored in SQLite -> clustering algo groups similar files -> proposal UI renders -> user approves -> fs.rename executes moves.
Data Model
FileIndex has many FileRecords. FileRecord has fields: path, embedding vector, proposed folder, status (pending/approved/rejected). User has one License.
Integration Points
OpenAI Embeddings API for semantic understanding, Stripe for payments, Gumroad as backup storefront, Sentry for crash reporting, Supabase for license key validation.
V1 Scope Boundaries
V1 excludes: Windows support, cloud sync, team shared vaults, Notion integration, mobile app, custom folder taxonomy editor.
Success Definition
A paying stranger downloads FolderWise, points it at their Downloads folder, approves the proposed structure, and posts a before/after screenshot on Twitter without any founder prompting.
Challenges
Distribution is hardest — macOS apps have cold discovery, and the App Store takes 30% while adding a review delay. Direct download with Gumroad works better but requires SEO and community trust before strangers install an app that touches their file system.
Avoid These Pitfalls
Do not move files without explicit user approval — one accidental destructive move will destroy trust and generate chargebacks. Do not send raw file content to OpenAI — embed locally chunked text only to avoid privacy backlash. Finding your first 10 paying customers will take longer than building the app — budget 3x more time on community demos than development.
Security Requirements
License key validated against Supabase on app start. No raw file content sent to any server. Rate limit OpenAI calls to 10 concurrent embeddings. Input validation on folder path selection to prevent symlink traversal.
Infrastructure Plan
Tauri desktop binary distributed via Gumroad. Landing page on Vercel. License key store on Supabase. Sentry Tauri SDK for crash reporting. GitHub Actions for automated .dmg builds on push to main.
Performance Targets
Index 100 files in under 30 seconds on M2 MacBook. Embedding API call under 500ms per file. Search results returned under 200ms from SQLite. UI renders proposal view under 1s for 200 file groups.
Go-Live Checklist
- ☐Security audit complete
- ☐Payment flow tested end-to-end
- ☐Sentry crash tracking live
- ☐No raw file content leaves device confirmed
- ☐Custom domain with SSL set up
- ☐Privacy policy and terms published
- ☐10 beta users signed off
- ☐Rollback plan documented
- ☐ProductHunt and r/macapps launch posts drafted.
How to build it, step by step
1. Run cargo tauri init folderwise and scaffold TypeScript frontend. 2. Add chokidar in frontend and wire to Tauri invoke command for file events. 3. Build embedder.ts calling OpenAI embeddings API on file text content. 4. Set up SQLite with better-sqlite3 storing path and embedding per file. 5. Implement cosine similarity clustering to group semantically related files. 6. Build ProposalView.tsx in v0 showing proposed folder structure with approve/reject per group. 7. Wire approve action to Tauri fs.rename commands in Rust. 8. Add natural language search bar querying SQLite by embedding similarity. 9. Build landing page with Lovable and Stripe Payment Link for $99 license. 10. Package as .dmg with tauri build and distribute via Gumroad.
Generated
April 3, 2026
Model
claude-sonnet-4-6